Open Cognitive Science

With collaboration as a main driver for scientific progress, I propose a series of methods implemented during my professional journey to develop, implement, and share open methods for cognitive science research. Inspired and educated by projects like the Open Science Framework and the Turing Way. During my education and professional career, I have personally benefited from the open science community and I am convinced of its advantage, specially for developing laboratories, and underpriviliged institutions. I contribute to experimental designs and the technology to execute them, analysis methods and the programming languages to implement them, as well as the means to communicate and share it all.

Experimental design

I have been programming cognitive experiments since my first training in Biofeedback and Neurofeedback in 2010. I have learned to use NBS Presentation and E-Prime, but have found that the most diverse and stable solution for most of my projects has always been acheived with Psychopy and OpenVIBE. Both of them are open-source and have a large community of users and developers. Designs like the Attentional Networks Test (ANT), the (N-Back task)[https://github.com/abcsds/Nback], and the Random Dot Motion Task (RDM), among others, can be found freely available on my github.

Technology for Cognitive Science

Along with many other scientists in the field, I have found a great solution for my lab can also be a solution for many other labs around the world. With the spirit of cooperation, Lab Streaming Layer (LSL) is largely used in my proposed solutions for cognitive experiments that require the measurement of multiple synchronized signals. With it, open standards like the Brain Imaging Data Structure (BIDS) and the Extensible Data Format (XDF) form part of the data management and sharing solutions I propose. These implementations allow for a large number of devices to be used in the same experiment, and for the data to be shared and analyzed in a standardized way.

Data analysis

Open source is first nature in software development. The world of computing just wouldn’t exist as it is without it. The NumFocus project knows that. Throughout my carreer they have been a great source of inspiration and support. I use the Python scientific stack and the Julia programming language for all my data analysis. I have found great use in MNE-Python, and PyTorch for my signal processing and deep learning needs.

Scientific communication

As the keystone of cooperation, scientific communication is the most important part of my work. I have used Jupyter notebooks throughout my entire higher education. Most recently, Quarto has fullfiled my needs for a more robust and reproducible scientific communication tool. With their help, git and GitHub have become strong tools for sharing and collaborating on my projects. This of course does not replace personal communication, in the form of courses, workshops, and conferences, but it does make it easier to share and collaborate in different languages and with continuously changing versions.

Science is a human right

Scientific knowledge is a common good. We can with the right mindset and tools, make its discovery, management, and sharing available to everyone. I am here to demonstrate it with my work, and to help others do the same.

About me

My name is Luis Alberto Barradas Chacón, I am a PhD candidate at doctoral school for Informatics at the TUGraz. I am a certified Mindfulness facilitator, and a member of the Mexican Society for Bio- and Neurofeedback, and the Mexican Biosignals group. In this webpage you might find some of my work as an Engineer, Behavioral Data Analyist, Statistician, and Cognitive Scientist.